code: def load_data_bert(
      config,
      train_examples: List[InputExample] = None,
      eval_examples: List[InputExample] = None,
      test_examples: List[InputExample] = None,
  ):
      train_data = (
          trans_BIO_Dataset(config, train_examples, "train")
          if train_examples
          else None
      )
      dev_data = (
          trans_BIO_Dataset(config, eval_examples, "dev")
          if eval_examples
          else None
      )
      test_data = (
          trans_BIO_Dataset(config, test_examples, "test")
          if test_examples
          else None
      )
  
      tokenizer = AutoTokenizer.from_pretrained(config.bert_name)
  
      vocab = Vocabulary()
      train_ent_num = (
          fill_vocab(vocab, train_data) if train_data else None
      )
      dev_ent_num = fill_vocab(vocab, dev_data) if dev_data else None
      test_ent_num = fill_vocab(vocab, test_data) if test_data else None
  
      table = pt.PrettyTable(["Dataset", "sentences", "entities"])
      if train_data:
          table.add_row(["train", len(train_data), train_ent_num])
      if dev_data:
          table.add_row(["dev", len(dev_data), dev_ent_num])
      if test_data:
          table.add_row(["test", len(test_data), test_ent_num])
      config.logger.info("\n{}".format(table))
  
      config.label_num = len(vocab.label2id)
      print(vocab.label2id)
      config.vocab = vocab
  
      train_dataset = (
          RelationDataset(
              *process_bert(train_data, tokenizer, vocab, "train")
          )
          if train_data
          else None
      )
      dev_dataset = (
          RelationDataset(
              *process_bert(dev_data, tokenizer, vocab, "dev")
          )
          if dev_data
          else None
      )
      test_dataset = (
          RelationDataset(
              *process_bert(test_data, tokenizer, vocab, "test")
          )
          if test_data
          else None
      )
      return (train_dataset, dev_dataset, test_dataset), (
          train_data,
          dev_data,
          test_data,
      )
  
imports: import json
  import torch
  from torch.utils.data import Dataset
  from torch.nn.utils.rnn import pad_sequence
  import numpy
  import prettytable
  from tqdm import tqdm
  from transformers import AutoTokenizer
  import os
  from deepke.name_entity_re.standard.tools import *
  import requests
  from typing import List
  from .utils import convert_index_to_text
label_desc: Use image node_id 5 for calling a global function with example usage: load_data_bert(config, train_examples, eval_examples, test_examples) and returns: train_dataset,  dev_dataset,  test_dataset,  train_data,  dev_data,  test_data